require(xgboost)
require(Matrix)
require(data.table)
if (!require(vcd)) {
  install.packages('vcd') #Available in CRAN. Used for its dataset with categorical values.
  require(vcd)
}
# According to its documentation, XGBoost works only on numbers.
# Sometimes the dataset we have to work on have categorical data.
# A categorical variable is one which have a fixed number of values.
# By example, if for each observation a variable called "Colour" can have only
# "red", "blue" or "green" as value, it is a categorical variable.
#
# In R, categorical variable is called Factor.
# Type ?factor in console for more information.
#
# In this demo we will see how to transform a dense dataframe with categorical variables to a sparse matrix
# before analyzing it in XGBoost.
# The method we are going to see is usually called "one hot encoding".

#load Arthritis dataset in memory.
data(Arthritis)

# create a copy of the dataset with data.table package
# (data.table is 100% compliant with R dataframe but its syntax is a lot more consistent
# and its performance are really good).
df <- data.table(Arthritis, keep.rownames = FALSE)

# Let's have a look to the data.table
cat("Print the dataset\n")
print(df)

# 2 columns have factor type, one has ordinal type
# (ordinal variable is a categorical variable with values which can be ordered, here: None > Some > Marked).
cat("Structure of the dataset\n")
str(df)

# Let's add some new categorical features to see if it helps.
# Of course these feature are highly correlated to the Age feature.
# Usually it's not a good thing in ML, but Tree algorithms (including boosted trees) are able to select the best features,
# even in case of highly correlated features.

# For the first feature we create groups of age by rounding the real age.
# Note that we transform it to factor (categorical data) so the algorithm treat them as independent values.
df[, AgeDiscret := as.factor(round(Age / 10, 0))]

# Here is an even stronger simplification of the real age with an arbitrary split at 30 years old.
# I choose this value based on nothing.
# We will see later if simplifying the information based on arbitrary values is a good strategy
# (I am sure you already have an idea of how well it will work!).
df[, AgeCat := as.factor(ifelse(Age > 30, "Old", "Young"))]

# We remove ID as there is nothing to learn from this feature (it will just add some noise as the dataset is small).
df[, ID := NULL]

# List the different values for the column Treatment: Placebo, Treated.
cat("Values of the categorical feature Treatment\n")
print(levels(df[, Treatment]))

# Next step, we will transform the categorical data to dummy variables.
# This method is also called one hot encoding.
# The purpose is to transform each value of each categorical feature in one binary feature.
#
# Let's take, the column Treatment will be replaced by two columns, Placebo, and Treated.
# Each of them will be binary.
# For example an observation which had the value Placebo in column Treatment before the transformation will have, after the transformation,
# the value 1 in the new column Placebo and the value 0 in the new column  Treated.
#
# Formulae Improved~.-1 used below means transform all categorical features but column Improved to binary values.
# Column Improved is excluded because it will be our output column, the one we want to predict.
sparse_matrix <- sparse.model.matrix(Improved ~ . - 1, data = df)

cat("Encoding of the sparse Matrix\n")
print(sparse_matrix)

# Create the output vector (not sparse)
# 1. Set, for all rows, field in Y column to 0;
# 2. set Y to 1 when Improved == Marked;
# 3. Return Y column
output_vector <- df[, Y := 0][Improved == "Marked", Y := 1][, Y]

# Following is the same process as other demo
cat("Learning...\n")
bst <- xgb.train(data = xgb.DMatrix(sparse_matrix, label = output_vector), max_depth = 9,
                 eta = 1, nthread = 2, nrounds = 10, objective = "binary:logistic")

importance <- xgb.importance(feature_names = colnames(sparse_matrix), model = bst)
print(importance)
# According to the matrix below, the most important feature in this dataset to predict if the treatment will work is the Age.
# The second most important feature is having received a placebo or not.
# The sex is third.
# Then we see our generated features (AgeDiscret). We can see that their contribution is very low (Gain column).

# Does these result make sense?
# Let's check some Chi2 between each of these features and the outcome.

print(chisq.test(df$Age, df$Y))
# Pearson correlation between Age and illness disappearing is 35

print(chisq.test(df$AgeDiscret, df$Y))
# Our first simplification of Age gives a Pearson correlation of 8.

print(chisq.test(df$AgeCat, df$Y))
# The perfectly random split I did between young and old at 30 years old have a low correlation of 2.
# It's a result we may expect as may be in my mind > 30 years is being old (I am 32 and starting feeling old, this may explain that),
# but for the illness we are studying, the age to be vulnerable is not the same.
# Don't let your "gut" lower the quality of your model. In "data science", there is science :-)

# As you can see, in general destroying information by simplifying it won't improve your model.
# Chi2 just demonstrates that.
# But in more complex cases, creating a new feature based on existing one which makes link with the outcome
# more obvious may help the algorithm and improve the model.
# The case studied here is not enough complex to show that. Check Kaggle forum for some challenging datasets.
# However it's almost always worse when you add some arbitrary rules.
# Moreover, you can notice that even if we have added some not useful new features highly correlated with
# other features, the boosting tree algorithm have been able to choose the best one, which in this case is the Age.
# Linear model may not be that strong in these scenario.
